{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8f40d109",
   "metadata": {},
   "source": [
    "# DAY 38"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66084126",
   "metadata": {},
   "source": [
    "在遇到大规模数据集时，显存常常无法一次性存储所有数据，所以需要使用分批训练的方法。为此，PyTorch提供了DataLoader类，该类可以自动将数据集切分为多个批次batch，并支持多线程加载数据。此外，还存在Dataset类，该类可以定义数据集的读取方式和预处理方式。\n",
    "\n",
    "\n",
    "1. DataLoader类：决定数据如何加载\n",
    "2. Dataset类：告诉程序去哪里找数据，如何读取单个样本，以及如何预处理。\n",
    "\n",
    "\n",
    "\n",
    "为了引入这些概念，我们现在接触一个新的而且非常经典的数据集：MNIST手写数字数据集。该数据集包含60000张训练图片和10000张测试图片，每张图片大小为28*28像素，共包含10个类别。因为每个数据的维度比较小，所以既可以视为结构化数据，用机器学习、MLP训练，也可以视为图像数据，用卷积神经网络训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "16defbf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x21354ca3090>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader , Dataset # DataLoader 是 PyTorch 中用于加载数据的工具\n",
    "from torchvision import datasets, transforms # torchvision 是一个用于计算机视觉的库，datasets 和 transforms 是其中的模块\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置随机种子，确保结果可复现\n",
    "torch.manual_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc18d74",
   "metadata": {},
   "source": [
    "```\n",
    "torchvision\n",
    "├── datasets       # 视觉数据集（如 MNIST、CIFAR）\n",
    "├── transforms     # 视觉数据预处理（如裁剪、翻转、归一化）\n",
    "├── models         # 预训练模型（如 ResNet、YOLO）\n",
    "├── utils          # 视觉工具函数（如目标检测后处理）\n",
    "└── io             # 图像/视频 IO 操作\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8837087e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 数据预处理，该写法非常类似于管道pipeline\n",
    "# transforms 模块提供了一系列常用的图像预处理操作\n",
    "\n",
    "# 先归一化，再标准化\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),  # 转换为张量并归一化到[0,1]\n",
    "    transforms.Normalize((0.1307,), (0.3081,))  # MNIST数据集的均值和标准差，这个值很出名，所以直接使用\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bbba82df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. 加载MNIST数据集，如果没有会自动下载\n",
    "train_dataset = datasets.MNIST(\n",
    "    root='./data',\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "test_dataset = datasets.MNIST(\n",
    "    root='./data',\n",
    "    train=False,\n",
    "    transform=transform\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a1c4ba",
   "metadata": {},
   "source": [
    "这里稍微有点反逻辑，正常思路应该是先有数据集，后续再处理。但是在pytorch的思路是，数据在加载阶段就处理结束。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf7890a",
   "metadata": {},
   "source": [
    "## 一、Dataset类\n",
    "\n",
    "现在我们想要取出来一个图片，看看长啥样，因为datasets.MNIST本质上集成了torch.utils.data.Dataset，所以自然需要有对应的方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c1a896e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 随机选择一张图片，可以重复运行，每次都会随机选择\n",
    "sample_idx = torch.randint(0, len(train_dataset), size=(1,)).item() # 随机选择一张图片的索引\n",
    "# len(train_dataset) 表示训练集的图片数量；size=(1,)表示返回一个索引；torch.randint() 函数用于生成一个指定范围内的随机数,item() 方法将张量转换为 Python 数字\n",
    "image, label = train_dataset[sample_idx] # 获取图片和标签"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12e4edfe",
   "metadata": {},
   "source": [
    "这里很难理解，为什么train_dataset[sample_idx]可以获取到图片和标签，是因为 datasets.MNIST这个类继承了torch.utils.data.Dataset类，这个类中有一个方法__getitem__，这个方法会返回一个tuple，tuple中第一个元素是图片，第二个元素是标签。\n",
    "\n",
    "我们来详细介绍下torch.utils.data.Dataset类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ddf07c0",
   "metadata": {},
   "source": [
    "PyTorch 的torch.utils.data.Dataset是一个抽象基类，所有自定义数据集都需要继承它并实现两个核心方法：\n",
    "\n",
    "- __len__()：返回数据集的样本总数。\n",
    "- __getitem__(idx)：根据索引idx返回对应样本的数据和标签。\n",
    "\n",
    "PyTorch 要求所有数据集必须实现__getitem__和__len__，这样才能被DataLoader等工具兼容。这是一种接口约定，类似函数参数的规范。这意味着，如果你要创建一个自定义数据集，你需要实现这两个方法，否则PyTorch将无法识别你的数据集。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a6cb9c",
   "metadata": {},
   "source": [
    "在 Python 中，__getitem__和__len__ 是类的特殊方法（也叫魔术方法 ），它们不是像普通函数那样直接使用，而是需要在自定义类中进行定义，来赋予类特定的行为。以下是关于这两个方法具体的使用方式："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27b2d3ff",
   "metadata": {},
   "source": [
    "### __getitem__方法\n",
    "__getitem__方法用于让对象支持索引操作，当使用[]语法访问对象元素时，Python 会自动调用该方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dea49d2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "# 示例代码\n",
    "class MyList:\n",
    "    def __init__(self):\n",
    "        self.data = [10, 20, 30, 40, 50]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx]\n",
    "\n",
    "# 创建类的实例\n",
    "my_list_obj = MyList()\n",
    "# 此时可以使用索引访问元素，这会自动调用__getitem__方法\n",
    "print(my_list_obj[2])  # 输出：30"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd2cf1a",
   "metadata": {},
   "source": [
    "通过定义__getitem__方法，让MyList类的实例能够像 Python 内置的列表一样使用索引获取元素。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f05d657",
   "metadata": {},
   "source": [
    "### __len__方法\n",
    "\n",
    "__len__方法用于返回对象中元素的数量，当使用内置函数len()作用于对象时，Python 会自动调用该方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3f2134dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "class MyList:\n",
    "    def __init__(self):\n",
    "        self.data = [10, 20, 30, 40, 50]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "# 创建类的实例\n",
    "my_list_obj = MyList()\n",
    "# 使用len()函数获取元素数量，这会自动调用__len__方法\n",
    "print(len(my_list_obj))  # 输出：5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d17efb8",
   "metadata": {},
   "source": [
    "这里定义的__len__方法，使得MyList类的实例可以像普通列表一样被len()函数调用获取长度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a35c33bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# minist数据集的简化版本\n",
    "class MNIST(Dataset):\n",
    "    def __init__(self, root, train=True, transform=None):\n",
    "        # 初始化：加载图片路径和标签\n",
    "        self.data, self.targets = fetch_mnist_data(root, train) # 这里假设 fetch_mnist_data 是一个函数，用于加载 MNIST 数据集的图片路径和标签\n",
    "        self.transform = transform # 预处理操作\n",
    "        \n",
    "    def __len__(self): \n",
    "        return len(self.data)  # 返回样本总数\n",
    "    \n",
    "    def __getitem__(self, idx): # 获取指定索引的样本\n",
    "        # 获取指定索引的图像和标签\n",
    "        img, target = self.data[idx], self.targets[idx]\n",
    "        \n",
    "        # 应用图像预处理（如ToTensor、Normalize）\n",
    "        if self.transform is not None: # 如果有预处理操作\n",
    "            img = self.transform(img) # 转换图像格式\n",
    "        # 这里假设 img 是一个 PIL 图像对象，transform 会将其转换为张量并进行归一化\n",
    "            \n",
    "        return img, target  # 返回处理后的图像和标签"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "id": "246b622e",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8346b443",
   "metadata": {},
   "source": [
    "- Dataset = 厨师（准备单个菜品）\n",
    "- DataLoader = 服务员（将菜品按订单组合并上桌）\n",
    "  \n",
    "预处理（如切菜、调味）属于厨师的工作，而非服务员。所以在dataset就需要添加预处理步骤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "10218423",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label: 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化原始图像（需要反归一化）\n",
    "def imshow(img):\n",
    "    img = img * 0.3081 + 0.1307  # 反标准化\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(npimg[0], cmap='gray') # 显示灰度图像\n",
    "    plt.show()\n",
    "\n",
    "print(f\"Label: {label}\")\n",
    "imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776d7c8f",
   "metadata": {},
   "source": [
    "## 二、Dataloader类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1815e9e1",
   "metadata": {},
   "source": [
    "该类比较简单，很好理解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "861bd261",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 创建数据加载器\n",
    "train_loader = DataLoader(\n",
    "    train_dataset,\n",
    "    batch_size=64, # 每个批次64张图片,一般是2的幂次方，这与GPU的计算效率有关\n",
    "    shuffle=True # 随机打乱数据\n",
    ")\n",
    "\n",
    "test_loader = DataLoader(\n",
    "    test_dataset,\n",
    "    batch_size=1000 # 每个批次1000张图片\n",
    "    # shuffle=False # 测试时不需要打乱数据\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b037b4",
   "metadata": {},
   "source": [
    "## 三、总结"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e97b46d0",
   "metadata": {},
   "source": [
    "\n",
    "| **维度**         | **`Dataset`**                              | **`DataLoader`**                          |\n",
    "|------------------|--------------------------------------------|-------------------------------------------|\n",
    "| **核心职责**      | 定义“数据是什么”和“如何获取单个样本”       | 定义“如何批量加载数据”和“加载策略”        |\n",
    "| **核心方法**      | `__getitem__`（获取单个样本）、`__len__`（样本总数） | 无自定义方法，通过参数控制加载逻辑        |\n",
    "| **预处理位置**    | 在`__getitem__`中通过`transform`执行预处理 | 无预处理逻辑，依赖`Dataset`返回的预处理后数据 |\n",
    "| **并行处理**      | 无（仅单样本处理）                         | 支持多进程加载（`num_workers>0`）          |\n",
    "| **典型参数**      | `root`（数据路径）、`transform`（预处理）  | `batch_size`、`shuffle`、`num_workers`     |\n",
    "\n",
    "**核心结论**\n",
    "- **`Dataset`类**：定义数据的**内容和格式**（即“如何获取单个样本”），包括：\n",
    "  - 数据存储路径/来源（如文件路径、数据库查询）。\n",
    "  - 原始数据的读取方式（如图像解码为PIL对象、文本读取为字符串）。\n",
    "  - 样本的预处理逻辑（如裁剪、翻转、归一化等，通常通过`transform`参数实现）。\n",
    "  - 返回值格式（如`(image_tensor, label)`）。\n",
    "\n",
    "- **`DataLoader`类**：定义数据的**加载方式和批量处理逻辑**（即“如何高效批量获取数据”），包括：\n",
    "  - 批量大小（`batch_size`）。\n",
    "  - 是否打乱数据顺序（`shuffle`）。\n",
    " \n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "DL",
   "language": "python",
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